AIMC Topic: Data Mining

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A Framework for Identifying Genotypic Information from Clinical Records: Exploiting Integrated Ontology Structures to Transfer Annotations between ICD Codes and Gene Ontologies.

IEEE/ACM transactions on computational biology and bioinformatics
Although some methods are proposed for automatic ontology generation, none of them address the issue of integrating large-scale heterogeneous biomedical ontologies. We propose a novel approach for integrating various types of ontologies efficiently a...

An Unsupervised Graph Based Continuous Word Representation Method for Biomedical Text Mining.

IEEE/ACM transactions on computational biology and bioinformatics
In biomedical text mining tasks, distributed word representation has succeeded in capturing semantic regularities, but most of them are shallow-window based models, which are not sufficient for expressing the meaning of words. To represent words usin...

Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection.

IEEE/ACM transactions on computational biology and bioinformatics
Recently, feature selection and dimensionality reduction have become fundamental tools for many data mining tasks, especially for processing high-dimensional data such as gene expression microarray data. Gene expression microarray data comprises up t...

Feasibility of 30-day hospital readmission prediction modeling based on health information exchange data.

International journal of medical informatics
INTRODUCTION: Unplanned 30-day hospital readmission account for roughly $17 billion in annual Medicare spending. Many factors contribute to unplanned hospital readmissions and multiple models have been developed over the years to predict them. Most r...

Textual inference for eligibility criteria resolution in clinical trials.

Journal of biomedical informatics
Clinical trials are essential for determining whether new interventions are effective. In order to determine the eligibility of patients to enroll into these trials, clinical trial coordinators often perform a manual review of clinical notes in the e...

Comparison of UMLS terminologies to identify risk of heart disease using clinical notes.

Journal of biomedical informatics
The second track of the 2014 i2b2 challenge asked participants to automatically identify risk factors for heart disease among diabetic patients using natural language processing techniques for clinical notes. This paper describes a rule-based system ...

A hybrid model for automatic identification of risk factors for heart disease.

Journal of biomedical informatics
Coronary artery disease (CAD) is the leading cause of death in both the UK and worldwide. The detection of related risk factors and tracking their progress over time is of great importance for early prevention and treatment of CAD. This paper describ...

An automatic system to identify heart disease risk factors in clinical texts over time.

Journal of biomedical informatics
Despite recent progress in prediction and prevention, heart disease remains a leading cause of death. One preliminary step in heart disease prediction and prevention is risk factor identification. Many studies have been proposed to identify risk fact...

Machine learning classification of medication adherence in patients with movement disorders using non-wearable sensors.

Computers in biology and medicine
Medication non-adherence is a major concern in the healthcare industry and has led to increases in health risks and medical costs. For many neurological diseases, adherence to medication regimens can be assessed by observing movement patterns. Howeve...

Extracting Biomedical Event with Dual Decomposition Integrating Word Embeddings.

IEEE/ACM transactions on computational biology and bioinformatics
Extracting biomedical event from literatures has attracted much attention recently. By now, most of the state-of-the-art systems have been based on pipelines which suffer from cascading errors, and the words encoded by one-hot are unable to represent...